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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ta–Zr carbides: Synthesis advances via carbothermal reduction and defect evolution observed through transmission electron microscopy ion irradiation

The thermodynamic stability of six distinct compositions within the Zr—Ta—C ternary system is investigated in this study, marking the first report of their synthesis through carbothermic reduction in vacuum. A prolonged annealing process at 2200°C enabled high densification and phase equilibrium. Detailed phase identification and microstructural characterization through microscopy and X-ray diffraction techniques revealed clear compositional trends and stable phase formations. Two compositions ((Ta 0.2 Zr 0.8 )C 0.6 and (Ta 0.5 Zr 0.5 )C 1 ) were selected for ion irradiation experiments using 200 keV Kr + at 600°C—representing the first-ever irradiation study on the Zr—Ta—C system. The findings indicated defect accumulation and nanoscale cavity formation without any evidence of amorphization, highlighting the system's structural stability under irradiation. Together, the synthesis and irradiation results provide a basis for further investigation of the system and suggest its relevance for applications under extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2020

Multilevel Convergence Analysis of Multigrid-Reduction-in-Time

This study presents a multilevel convergence framework for multigrid-reduction-in-time (MGRIT) as a generalization of previous two-grid estimates. The framework provides a priori upper bounds on the convergence of MGRIT V- and F-cycles, with different relaxation schemes, by deriving the respective residual and error propagation operators. The residual and error operators are functions of the time-stepping operator, analyzed directly and bounded in the norm, both numerically and analytically. We present various upper bounds of different computational cost and varying sharpness. These upper bounds are complemented by proposing analytic formulae for the approximate convergence factor of V-cycle algorithms that take the number of fine grid time points, the temporal coarsening factors, and the eigenvalues of the time-stepping operator as parameters. The paper concludes with supporting numerical investigations of parabolic (anisotropic diffusion) and hyperbolic (wave equation) model problems. We assess the sharpness of the bounds and the quality of the approximate convergence factors. Observations from these numerical investigations demonstrate the value of the proposed multilevel convergence framework for estimating MGRIT convergence a priori and for the design of a convergent algorithm. We further highlight that observations in the literature are captured by the theory, including that two-level Parareal and multilevel MGRIT with F-relaxation do not yield scalable algorithms and the benefit of a stronger relaxation scheme. An important observation is that with increasing numbers of levels MGRIT convergence deteriorates for the hyperbolic model problem, while constant convergence factors can be achieved for the diffusion equation. The theory also indicates that L-stable Runge--Kutta schemes are more amendable to multilevel parallel-in-time integration with MGRIT than A-stable Runge--Kutta schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Scalable Multigrid Reduction Framework for Multiphase Poromechanics of Heterogeneous Media

Simulation of multiphase poromechanics involves solving a multiphysics problem in which multiphase flow and transport are tightly coupled with the porous medium deformation. To capture this dynamic interplay, fully implicit methods, also known as monolithic approaches, are usually preferred. The main bottleneck of a monolithic approach is that it requires solution of large linear systems that result from the discretization and linearization of the governing balance equations. Because such systems are nonsymmetric, indefinite, and highly ill-conditioned, preconditioning is critical for fast convergence. Recently, most efforts in designing efficient preconditioners for multiphase poromechanics have been dominated by physics-based strategies. Current state-of-the-art “black-box” solvers such as algebraic multigrid (AMG) are ineffective because they cannot effectively capture the strong coupling between the mechanics and the flow subproblems, as well as the coupling inherent in the multiphase flow and transport process. In this work, we develop an algebraic framework based on multigrid reduction (MGR) that is suited for tightly coupled systems of PDEs. Using this framework, the decoupling between the equations is done algebraically through defining appropriate interpolation and restriction operators. One can then employ existing solvers for each of the decoupled blocks or design a new solver based on knowledge of the physics. We demonstrate the applicability of our framework when used as a “black-box” solver for multiphase poromechanics. Here, we show that the framework is flexible to accommodate a wide range of scenarios, as well as efficient and scalable for large problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multigrid Reduction in Time for Chaotic Dynamical Systems

As CPU clock speeds have stagnated and high performance computers continue to have ever higher core counts, increased parallelism is needed to take advantage of these new architectures. Traditional serial time-marching schemes can be a significant bottleneck, as many types of simulations require large numbers of time-steps which must be computed sequentially. Parallel-in-time schemes, such as the Multigrid Reduction in Time (MGRIT) method, remedy this by parallelizing across time-steps and have shown promising results for parabolic problems. However, chaotic problems have proved more difficult, since chaotic initial value problems (IVPs) are inherently ill-conditioned. MGRIT relies on a hierarchy of successively coarser time-grids to iteratively correct the solution on the finest time-grid, but due to the nature of chaotic systems, small inaccuracies on the coarser levels can be greatly magnified and lead to poor coarse-grid corrections. Here we introduce a modified MGRIT algorithm based on an existing quadratically converging nonlinear extension to the multigrid Full Approximation Scheme (FAS), as well as a novel time-coarsening scheme. Together, these approaches better capture long-term chaotic behavior on coarse-grids and greatly improve convergence of MGRIT for chaotic IVPs. Further, we introduce a novel low-memory variant of the algorithm for solving chaotic PDEs with MGRIT which not only solves the IVP, but also provides estimates for the unstable Lyapunov vectors of the system. Finally, we provide supporting numerical results for the Lorenz system and demonstrate parallel speedup for the chaotic Kuramoto–Sivashinsky PDE over a significantly longer time-domain than in previous works.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Oxygen Reduction Reaction Activity of Nanocolumnar Platinum Thin Films by High Pressure Sputtering

Nanocolumnar platinum thin films (Pt-TFs) were produced by high pressure sputtering (HIPS) and investigated as oxygen reduction reaction electrocatalysts for polymer electrolyte membrane fuel cells. Conventional high-density Pt-TF prepared by low pressure sputtering was also studied for comparison. Pt-TFs were deposited on a microporous layer (MPL)-like surface composed of carbon particles to mimic catalyst-coated gas diffusion electrodes. Electron microscopy imaging revealed that HIPS Pt-TFs developed a cauliflower-like columnar microstructure, which originated from a shadowing effect during HIPS. This shadowing effect is enhanced on the rough surface of the MPL-like carbon that leads to the nano-cauliflower formation. With this approach, we also aimed to relate the catalyst performance obtained by benchtop tests directly to membrane electrode assembly test results. The electrochemically active surface area of Pt-TFs increased from 10 to 19 m 2 /g with increasing sputter pressure. Specific activity of conventional high-density and nanocolumnar films were similar at ~600 μA/cm 2 , which is likely due to their similar crystal grain sizes, >5 nm. On the other hand, mass-specific (MA) activity values increased from ~0.06 A/mgPt for conventional Pt-TF to ~0.13 A/mgPt for HIPS Pt-TFs, which is consistent with the columnar microstructure of HIPS films providing a better catalyst utilization compared to conventional Pt-TF.

08 HYDROGEN↗
DOE OSTI2021

Operando Topography and Mechanical Property Mapping of CO 2 Reduction Gas-Diffusion Electrodes Operating at High Current Densities

Electrochemical atomic force microscopy (EC-AFM) enables measurement of electrode topography and mechanical properties during electrochemical reactions. However, for aqueous-based reactions that make gas products, such as CO 2 reduction and water splitting into CO/H 2 , current densities below 1 mA cm −2 have been necessary to prevent formation of bubbles at the electrode; such bubbles can stick to the AFM probe and prevent further AFM imaging. Here, we demonstrate a novel cell design with a gas-diffusion electrode (GDE) to exhaust the gas products, thereby enabling high current density EC-AFM measurements at 1, 10, and 100 mA cm −2 that are not disturbed by bubble formation at the electrode surface. These experiments revealed a stable morphological structure of Cu catalysts deposited on GDEs during high current density operation. Systematic spatially resolved maps of deformation and adhesion showed no signs of a gas-liquid interface between catalyst particles of the GDE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Impact of Nickel Ions on the Oxygen Reduction Reaction Kinetics of Pt and on Oxygen Diffusion through Ionomer Thin Films

The effects of dissolved nickel on the oxygen reduction reaction (ORR) kinetics and oxygen transport properties of perfluorosulfonic acid (PFSA) thin films were investigated using rotating disk electrode (RDE) measurements of ORR on a PFSA-coated platinum electrode. The electrochemical characterization in 0.1 M perchloric acid (HClO 4 ) with and without added Ni 2+ quantitatively measured the impact of ionic interactions between the Ni 2+ cations and sulfonate (SO 3 - ) anions on oxygen transport through the PFSA thin film. Cyclic voltammetry (CV) curves in deaerated electrolyte showed that Ni 2+ cations diffusing through the PFSA thin film interact with the Pt surface altering the hydrogen underpotential deposition and stripping processes and decreasing ORR kinetics. The RDE limiting current results point to reduced permeability of oxygen through PFSA-Ni 2+ compared to PFSA-H + . Furthermore, the results indicate that transition metals leached from Pt alloy catalyst may be detrimental not only to the intrinsic ORR kinetics of the PEFC cathode catalyst through loss of the ORR-enhancing transition metal, but may also inhibit the diffusion of oxygen to the catalytic sites and poison the ORR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Mechanistic Insights into Oxygen Reduction Reaction on Metal Cathode Over BaZrO 3 -Based Electrolyte under Moist Atmosphere: Interfacial Interactions and Reaction Pathways

The oxygen reduction reaction (ORR) is a critical process that often governs the overall performance of electrochemical systems such as proton conducting solid oxide fuel cells. Despite its significance, the current understanding about the ORR, especially when it involves H 2 O is still limited. In this study, the proton involved ORR (PI-ORR) on metal cathode (e.g., Pt) over BaZrO 3 (BZO) electrolyte is investigated using density functional theory (DFT) under moist atmosphere. Two scenarios are considered: one at relatively high temperature (900 K) in the presence of oxygen vacancy in BZO substrate and one at relatively low temperature (700 K) without oxygen vacancy. We systematically explored oxygen vacancy effects on adsorption sites and proton transfer pathways, calculated energy profiles and Gibbs free energies for elementary steps along the O 2 dissociated and the O 2 concerted reaction pathways. We also evaluated H 2 O impacts on intermediate oxygen species (*O and *OH) through Ab Initio molecular dynamics. These analyses revealed pathway-dependent reaction mechanisms and hydration effects at triple-phase boundary, advancing fundamental understanding of PI-ORR in metal cathode/Ba-based electrolyte systems for energy applications.

08 - HYDROGEN↗
DOE OSTI2026

Optimized Tandem Catalyst Patterning for CO 2 Reduction Flow Reactors

Tandem catalysis involves two or more catalysts arranged in proximity within a single reaction vessel, with the aim of synergistically aligning the catalysts’ reaction pathways to maximize overall system performance. This study presents a proof of concept showing the integration of continuum transport modeling with design optimization in a simplified two-dimensional flow reactor setup for electrochemical CO 2 reduction. Ag catalysts provide the CO 2 ⟶ CO reaction capability, and Cu catalysts provide the CO ⟶ high-value products reaction capability. Given a set of input parameters, the optimization algorithm uses adjoint methods to modify the Ag/Cu surface patterning in order to maximize the current density toward high-value products, such as ethylene. The optimized designs yield significant performance enhancement especially at more negative applied voltages (i.e., stronger surface reactions) and for larger numbers of patterning sections. For an applied voltage of −1.7 V vs. SHE, the 12-section optimized design increases the current density toward ethylene by up to 65% compared to the unoptimized 2-section design. For the optimized cases, observed differences in the production and consumption of CO (the key intermediate species) and minimized zones of low CO reactant surface concentration on Cu sections explain the improved reactor performance.

CO2 reduction↗
DOE OSTI2025

Performance analysis and data reduction for exascale scientific workflows

Chimbuko is the first in situ, scalable, workflow-level performance analysis tool for trace-level analysis and visualization of application performance. This tool was developed by the Co-design Center for Online Data Analysis and Reduction and funded by the U.S. Department of Energy’s Exascale Computing Project. We provide a detailed description of Chimbuko’s architecture and illustrate our online and offline visualization with multiple use cases. We also present results for the deployment and scalability of the tool as applied to a high-energy physics workflow running at large scale on the Frontier supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multigrid reduction in time with Richardson extrapolation

The advent of exascale computing will leave many users with access to more computational resources than they can simultaneously use, e.g., billion-way parallelism. In particular, this is true for time-dependent simulations that limit parallelism to the spatial domain. One method to add parallelism in time to existing simulation codes and thus take advantage of ever larger compute resources is Multigrid Reduction in Time (MGRIT). The goal is to achieve a smaller time-to-solution through parallelism in time. In this paper, MGRIT is enhanced with Richardson extrapolation in a cost-efficient way to produce a parallel-in-time method with improved accuracy. Overall, this leads to a large improvement in the accuracy per computational cost of MGRIT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Transuranic Waste Volume Reduction Methods for Los Alamos National Laboratory

Los Alamos National Laboratory (LANL, or the Lab) is one of the United States’ (U.S.) weapons complex nuclear security laboratories; it is owned by the Department of Energy (DOE) and operated by Triad National Security, LLC. LANL’s mission is to solve national security challenges through scientific excellence, which includes nuclear deterrence and stockpile stewardship. Such activities generate a variety of radioactive waste types, including transuranic (TRU) waste. The National Nuclear Security Administration (NNSA) is under a congressional mandate to produce no fewer than 80 plutonium pits per year by 2030 to meet Department of Defense requirements and support the nuclear deterrent. LANL is responsible for producing at least 30 pits per year by 2026, while the Savannah River Site will produce 50 pits per year by 2030. As of August 2018, the NNSA TRU waste inventory at LANL was at 53% capacity. In order to ensure the nuclear material missions are able to continue uninterrupted, the storage of TRU waste needs to remain below 60% of total capacity. As LANL increases production and prepares to meet the 30 pits per year goal, waste generation will increase substantially. Thus, there is a need to rejuvenate current infrastructure and implement TRU waste reduction methods in the coming years.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Analysis of Conservation Voltage Reduction under Inverter-Based VAR-Support [Slides]

Conservation voltage reduction (CVR) is a common technique used by utilities to strategically reduce demand during peak periods. As penetration levels of distributed generation (DG) continue to rise and advanced inverter capabilities become more common, it is unclear how the effectiveness of CVR will be impacted and how CVR interacts with advanced inverter functions. In this work, we investigated the mutual impacts of CVR and DG from photovoltaic (PV) systems (with and without autonomous Volt-VAR enabled). The analysis was conducted on an actual utility dataset, including a feeder model, measurement data from smart meters and intelligent reclosers, and metadata for more than 30 CVR events triggered by the utility over the year. The installed capacity of the modeled PV systems represented 66% of peak load, but reached instantaneous penetrations reached up to 2.5x the load consumption over the year. While the objectives of CVR and autonomous Volt-VAR are opposed to one another, this study found that their interactions were mostly inconsequential since the CVR events occurred when total PV output was low.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

GHG Emissions Reduction Audit: A Checklist for Owners

This guidance and accompanying checklist detail recommended services and deliverables for a building-level greenhouse gas (GHG) emissions reduction audit focused on Scope 1 and 2 GHG emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Overview of the Inflation Reduction Act of 2022 (IRA) Home Energy Rebate Tool

The IRA Home Energy Rebate Tool (Tool) provides if/then analysis on the impact of two provisions of the Inflation Reduction Act (IRA); 50121 - Home Energy Performance-Based Whole-House Rebates (Home Efficiency Rebates or HOMES Program) and 50122 - High-Efficiency Electric Home Rebate Program (Home Electrification and Appliance Rebates or High-Efficiency Electric Appliances Rebates Program). The Tool can generate realistic scenarios based on the legislative text. The results of the Tool should not be interpreted as forecasts or predictions of what will happen for individual households, States or the nation. This overview provides a high level summary of the data inputs and methods.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Assessing the Impact of the Inflation Reduction Act on Nuclear Plant Power Uprate and Hydrogen Cogeneration

On August 16, 2022, Congress passed the Inflation Reduction Act (IRA) to promote investment in new, carbon-free power generation and sustainable operation of existing carbon-free assets. Specifically, the IRA includes both a production tax credit (PTC – Section 45Y of the IRA) and an investment tax credit (ITC – Section 48E) which utilities may leverage to offset the costs of power uprate. Further, the IRA includes a provision (Section 45V) for a PTC associated with carbon-free hydrogen cogeneration. These tax credits, along with recent legislation efforts to decarbonize the country, have re-emphasized the importance of maintaining and optimizing the existing nuclear plant operating fleet. As a result, utilities are reexamining the possibility of uprating their existing nuclear assets to further maximize carbon-free electricity generation.

08 HYDROGEN↗
DOE OSTI2023

Biofuel Production and Greenhouse Gas Reduction Potential

Under two biomass supply scenarios presented in this study, between 56 and 81 B GGE/year of biofuel could be produced. The corresponding GHG reduction potential is between to 410 million 635 billion Mt CO2e/year. This volume of biofuel is sufficient to replace a majority of fuel demand in hard-to-electrify aviation, marine, rail and off-road transportation by 2050. Through pathway selection and process design, sufficient sustainable aviation fuel (SAF) could be produced to exceed the projected 35 B gpy of domestic aviation fuel demand by 2050. The external hydrogen required to produce this volume of fuel is estimated to be between 3.5 to 5.2 Mt/year.

09 BIOMASS FUELS↗